Comprehensive analyses of the initiation and landslide-generated wave processes of the 24 June 2015 Hongyanzi landslide at the Three Gorges Reservoir,China

Reservoir landslides pose a great threat to shipping safety, human lives and properties, and the operation of the hydropower station. In this paper, the 24 June 2015 Hongyanzi landslide at the Three Gorges Reservoir is considered as an example to study the initiation mechanism and landslide-generated wave process of a reservoir landslide. The finite difference method and limit equilibrium analysis are used to analyze the deformation and failure characteristics of the Hongyanzi slope. Simulation results show that a large deformation (about 358 mm) happens in the shallow deposits under intermittent rainfall condition, and the slope is in a limit state. At the same time, continuous rapid drawdown of the water level (about ?0.55 m/day during 8–24 June 2015) reduced the support and accelerated the drainage of the water for the bank slope. A coupling effect of intermittent rainfall and rapid drawdown of the water level was the triggering factor of the 24 June Hongyanzi landslide. Landslide-generated wave process was simulated using a fluid–solid coupling method by integrating the general moving object collision model. Simulation results show that the landslide-generated wave is dominated by the impulse wave, which is generated by sliding masses entering the river with high speed. The maximum wave height is about 5.90 m, and the wave would decay gradually as it spreads because of friction and energy dissipation. To prevent reservoir landslides, the speed for the rising or drawdown of the water level should be controlled, and most importantly, rapid drawdown should be avoided.     

A physical similarity model of an impulsive wave generated by Gongjiafang landslide in Three Gorges Reservoir,China

Landslide-related impulse waves are catastrophic but accidental, so limited data on field measurements are available; scaled physical experiment is therefore a functional method to simulate and analyze this phenomenon. A large-scale physical Froude-similar model to produce impulse waves was constructed based on the Chinese Gongjiafang landslide, which occurred on the main stream of Three Gorges after the impounding in the reservoir in China. With a scale of 1:200, the model had the dimensions of 24, 8, and 1.3 m. Four water levels, 145, 156, 172.8, and 175 m, were modeled for the experiments, and marble coarse sands were used to imitate the actual cataclastic rock mass. Wave height gauges, high-speed cameras, and run-up measuring instruments were used to monitor wave fluctuations in the model. Among the experiments, the ones modeled using a water level of 172.8 m best confirmed the actual conditions in the Gongjiafang landslide, representing a good validation of the experiments. This study obtained, for the first time, specific data on the reproduced impulse waves’ convergence and superposition during propagation, and of the energy change between impulse waves and reflected waves. The test data describe a rapid decaying and gradual decaying rule for the wave heights and run-ups. The Froude-similar experiments presented in this article help us to understand the whole procedure of impulsive wave generated by cataclastic rock mass failure, and the results acquired contribute to studies of impulse waves caused by similar bank destabilizations worldwide.     

Mechanism of the Anlesi landslide in the Three Gorges Reservoir, China

Many gentle dip translational rock slides have taken place in the Three Gorges Reservoir, China. In order to study the mechanism of these translational rock slides, the authors use the Anlesi landslide as a typical case study to investigate in detail. Field investigations show that the slip zones of the Anlesi landslide were formed from a white mudstone in Jurassic red strata. X-ray diffraction and infrared ray analysis showed that the main mineral components of the slip zones are montmorillonite, illite, feldspar and quartz. Laboratory tests indicate that the slip zone soils are silty clay, of medium-swelling potential, the shear strength decreasing significantly as the slip zone attracts water and saturates.The main factors contributing to the Anlesi landslide are recent tectonic activity, incompetent beds, and intensive rainfall. Recent tectonic activity had caused shear failure along the incompetent beds, and joints within the sandstone. With the effect of intensive rainfall, water percolates to the incompetent beds along tectonic fissures, resulting in swelling of the soil material and high groundwater pressures within fissures in the strata. As a consequence, the Anlesi slope is prone to slide along these incompetent beds.Flac3D software was used to simulate the mechanism of the Anlesi landslide considering the rheological properties of soil and rock. The simulation results demonstrate that the stress, displacement and failure area changes with simulated creep time. The maximum displacement in the X direction reaches 7.59 m after 200-year simulated creep. Therefore, the mechanism of the Anlesi landslide can be illustrated considering the rheological properties of Jurassic red strata.     

The July 14, 2003 Qianjiangping landslide,Three Gorges Reservoir,China

The Qianjiangping landslide occurred after the first impoundment of the Three Gorges Reservoir in July 2003. Field investigation revealed that failure occurred when the reservoir reached 135 m, but the stability of the affected slope was already reduced by pre-existing bedding-plane shears, quarrying of mudstone from the landslide toe, and previous heavy rain. A possible explanation of the rapid and long runout mechanism of the landslide is that movement on a bedding-plane shear ruptured the calcite cement and rapidly reduced the sandstone strength to residual shear strength.     

Numerical study of the runout behavior of the Kamenziwan landslide in the Three Gorges Reservoir region,China

Landslides - The evaluation of landslide runout behavior plays a vital role in the risk assessment of landslides. In this study, the runout behavior of a landslide that occurred on December 10,...     

Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir,China

Different types of landslides exhibit distinct relationships with environmental conditioning factors. Therefore, in regions where multiple types of landslides coexist, it is required to separate landslide types for landslide susceptibility mapping (LSM). In this paper, a landslide-prone area located in Chongqing Province within the middle and upper reaches of the Three Gorges Reservoir area (TGRA), China, was selected as the study area. 733 landslides were classified into three types: reservoir-affected landslides, non-reservoir-affected landslides, and rockfalls. Four landslide inventory datasets and 15 landslide conditional factors were trained by three Machine Learning models (logistic regression, random forest, support vector machine), and a Deep Learning (DL) model. After comparing the models using receiver operating characteristics (ROC), the landslide susceptibility indexes of three types landslides were acquired by the best performing model. These indexes were then used as input to generate the final map based on the Stacking method. The results revealed that DL model showed the best performance in LSM without considering landslide types, achieving an area under the curve (AUC) of 0.854 for testing and 0.922 for training. Moreover, when we separated the landslide types for LSM, the AUC improved by 0.026 for testing and 0.044 for training. Thus, this paper demonstrates that considering different landslide types in LSM can significantly improve the quality of landslide susceptibility maps. These maps in turn, can be valuable tools for evaluating and mitigating landslide hazards.     

Establishing a monitoring network for an impoundment-induced landslide in Three Gorges Reservoir Area, China

A methodology for monitoring system of an impoundment-induced landslide in Three Gorges Reservoir Area, China is introduced. Currently, based on landslide geological classification, the monitoring regions and methods which include types of monitoring instruments, placement and calibration precision of instruments, and appropriate periods for instrumental placement is confirmed. To optimize the monitoring system, sensitivity analysis of displacements and the water table in landslides affected by reservoir surface fluctuation is completed to determine the layout of the monitoring cross sections and the monitoring points. As a case study, the behavior of displacements and the potential fluctuation of the water table in the Shiliushubao landslide, produced by the gradual water impoundment at Three Gorges Reservoir, has been simulated using 3D finite element method analysis. The sensitivity analysis of Shiliushubao landslide is investigated by the fuzzy set evaluation method. As a result, the monitoring network of Shiliushubao landslide is established.     

Analysis of waves generated by Gongjiafang landslide in Wu Gorge, three Gorges reservoir, on November 23, 2008

At 4:40p.m. on November 23, 2008, the Gongjiafang slope collapsed on the north bank of Yangtze River in Wu Gorge of Three Gorges Reservoir. The 380,000-m³ sliding mass consisted mainly of cataclastic rock. A video record of the major sliding incident was analyzed using the general laws of physical motion. The analysis indicated that the maximum speed and maximum acceleration of the sliding mass were 11.65?m/s and 2.23?m/s², respectively, and that the maximum amplitude and the propagation velocity of the water wave near the landslide were 31.8?m and 18.36?m/s, respectively. Wave run-up investigation indicated that the maximum run-up on shore was 13.1?m, which declined to 1.1?m at Wushan dock 4?km away. The incident causes no casualties, but did result in economic losses of RMB five million. The numerical simulation model GEOWAVE was used to simulate and reproduced the impulse wave generated by the landslide; the results were in good agreement with the observed incident. The numerical simulation data were then applied to analyze the decay and amplification effects of the landslide wave in the river course. The field investigations and witness information provide valuable materials for the studies of landslide kinematics and impulse waves generated by landslides. In addition, the research results provide a useful reference for future similar waves generated by landslides in reservoirs.     

三峡水库区万州独生基滑坡稳定性分析

独生基滑坡位于重庆市万州区长江右岸。为百安坝斜坡松散堆积层沿下伏软弱层面滑动的滑坡体。属于降雨及地表生活排水诱发、人类工程活动加剧变形而形成的新滑坡。论文在分析和研究滑坡区地质环境背景、平剖面形态、特征及诱发因素的基础上,采用传递系数法计算了滑坡体各工况及荷载组合条件下的稳定系数。通过计算,滑坡在天然状态、加载及暴雨条件下均处于不稳定状态。最后根据滑坡特征、主要诱发因素及其稳定性,针对滑坡体失稳特征,通过防治方案对比,初步拟定以抗滑桩工程进行治理,以保证滑坡体的稳定。     

三峡库区黄土坡滑坡降雨入渗模型研究

传统的入渗模型未考虑坡角和降雨强度对滑坡入渗过程的影响,为了更好地描述黄土坡滑坡降雨入渗过程,在Green- Ampt入渗模型的基础上推导了考虑坡体倾角和小降雨强度影响的降雨入渗模型。为了获取改进的入渗模型参数,在黄土坡滑坡1#崩滑体上进行了双环渗透试验与降雨、土的含水率和基质吸力现场监测。结果表明,黄土坡滑坡1#崩滑体饱和渗透系数为4.81×10-5 m/s;降雨时体积含水率增加,降雨停止后体积含水率降低,深部表现出一定的滞后特性;基质吸力变化趋势与体积含水率相反,降雨使其减小,降雨停止后逐渐增大。通过双环渗透试验与现场监测,获取了黄土坡滑坡降雨入渗模型参数值。将入渗模型计算值与现场监测数据进行对比,该模型计算值与现场监测数据吻合,说明该降雨入渗模型可用于黄土坡滑坡降雨入渗分析。     

Probabilistic forecasting of landslide displacement accounting for epistemic uncertainty: a case study in the Three Gorges Reservoir area,China

Accurate and reliable displacement forecasting plays a key role in landslide early warning. However, due to the epistemic uncertainties associated with landslide systems, errors are unavoidable and sometimes significant in traditional methods of deterministic point forecasting. Transforming traditional point forecasting into probabilistic forecasting is essential for quantifying the associated uncertainties and improving the reliability of landslide displacement forecasting. This paper proposes a hybrid approach based on bootstrap, extreme learning machine (ELM), and artificial neural network (ANN) methods to quantify the associated uncertainties via probabilistic forecasting. The hybrid approach consists of two steps. First, a bootstrap-based ELM is applied to estimate the true regression mean of landslide displacement and the corresponding variance of model uncertainties. Second, an ANN is used to estimate the variance of noise. Reliable prediction intervals (PIs) can be computed by combining the true regression mean, variance of model uncertainty, and variance of noise. The performance of the proposed hybrid approach was validated using monitoring data from the Shuping landslide, Three Gorges Reservoir area, China. The obtained results suggest that the Bootstrap-ELM-ANN approach can be used to perform probabilistic forecasting in the medium term and long term and to quantify the uncertainties associated with landslide displacement forecasting for colluvial landslides with step-like deformation in the Three Gorges Reservoir area.     

Experimental modeling of tsunamis generated by subaerial landslides: two case studies of the Three Gorges Reservoir,China

After the Gongjiafang tsunami incident, the China Geological Survey recognized the extreme importance of subaerial landslide-generated tsunamis (SLGT) in the Three Gorges Reservoir, western China. The experimental models presented in the present paper were rooted in two common failure types of high, steep slopes in the Three Gorges Reservoir. One model represents a rigid rock plunging into the water and the other is a granular cluster sliding into the water. A wide range of effective parameters were considered and studied by performing 74 laboratory tests. The effects of bed slope angle, water depth, slide impact velocity, geometry (three-dimensional size of the rigid block and grain size of the granular cluster), and volume on impulse wave characteristics were examined. Slide kinematics and impulsive wave features, such as wave shape, amplitude, run-up, and run-down, were studied and compared. Experiments showed that the failure type of the rock mass plays a key role in SLGT and strongly influences air–fluid–solid interaction. Thus, to forecast SLGT rapidly, the zone of complicated air–solid–fluid interaction can be treated as a “black box” whose output is a water wave with definable characteristics, such as amplitude. Based on the experimental results, two dimensionless equations for the estimation of primary wave maximum amplitude were developed and successfully verified for the two failure types. The study provides basic information needed to forecast tsunamis generated by slopes with the potential to fail in other reservoirs around the world. These data can also be a very useful resource for theoretical analysis or numerical model validation.     

三峡大树场镇堆积层滑坡暴雨失稳机理研究

2014年9月1日,奉节大树场镇发生了大型崩塌堆积层滑坡,体积约37.8×104m3。基于大量的现场地质调查和室内分析,文章阐述了滑坡特殊的地质地貌及地质结构特征,分别从地质和环境因素两个方面进行了滑坡成因分析,采用非稳态非饱和渗流方法研究滑动失稳变形过程,并基于地貌学与工程地质力学理论推断滑坡形成演化过程。得出如下结论:(1)大树场镇滑坡为极端降雨诱发的近水平地层区崩塌堆积层滑坡,具有多次滑动特征;(2)特殊的地形地貌条件是滑坡形成的内因;极端强降雨是滑坡发生的直接诱发因素;(3)降雨前期,降雨主要通过入渗滑体增加其自重,入渗量大而渗流量小,对滑体稳定性影响较小;之后持续强降雨致使基岩顶面处的孔隙水压力不断增大,为滑体提供动态浮托力,加之滑体内部形成的渗流场导致稳定性大幅度降低;堆积体结构分布受控于基岩接触面形态,滑体不同区域达到饱和状态时间存在明显差异性是多次滑动变形破坏的理论依据;(4)短时极端降雨触发堆积层局部饱和岩土体由蠕滑逐步形成小规模滑坡碎屑流;持续极端降雨导致基岩顶面富集大量地下水,使堆积层岩土体呈饱和-过饱和状态,形成大规模滑坡碎屑流。     

三峡库区白衣庵滑坡防治工程研究

白衣庵滑坡是一个以古崩滑体为主体的古、老、新滑坡组成的滑坡群。白衣阉滑坡防治工程的目标是避免暴雨或持续降雨条件下滑坡的局部复活，提高三峡水库蓄水后水位变动条件下滑坡的整体稳定性。通过3种防治工程方案比选论证，确定白衣庵滑坡的治理工程方案为地表排水、后缘削方减载和前缘填方反压。滑坡稳定性校核计算表明，治理工程实施后在特殊荷载条件下滑坡主剖面的稳定性系数达到设计标准1.15-1.2。按审查通过的工程方案分别进行了工程设计和滑坡治理工程效果监测设计。     

三峡水库水位变动下的库岸滑坡稳定性评价

水库岸坡滑坡稳定性主要受库水位涨落的影响。由于库区水位变化可概化为二维非稳定流,地下水位变化可采用有限元模拟。三峡水库正常运行时的水位涨落速度在0.6～4.0m/d、高程145～175m之间变化,通过有限元法对库区的马家沟滑坡模拟表明:库水位和滑坡体内的地下水位同步升降,水力梯度很小,因此水位涨落对滑坡的影响主要是浮托力作用。在此条件下,采用Morgenstern-Price法对滑坡稳定性进行计算表明,随着水位上升,滑坡稳定性降低,水位上升到165m时,稳定性达到最小,水位再上升则稳定性增大,当滑坡完全淹没在水下时的稳定性高于未被淹没的情况,滑坡最终的稳定性按最小稳定系数评价。     

三峡库区藕塘滑坡特征、成因及形成机制研究

三峡库区藕塘滑坡位于重庆市奉节县安坪镇长江右岸,是由于三峡水库蓄水引起的巨型顺层基岩滑坡,面积约1.78 km2,体积约9.0×107m3,是近年来三峡库区重大滑坡灾害之一。基于大量的野外地质调查以及室内试验分析,详尽阐述了滑坡地质地貌及地质结构特征,运用地貌学与工程地质力学理论方法从地质和环境因素两方面进行滑坡成因分析,提出了藕塘滑坡的形成机制及演化过程,并对其变形发展的趋势进行了预测,得出如下结论:(1)藕塘滑坡为巨型顺层岩质滑坡,平面形态具有多级多期次滑动特征,空间形态具有视向倾斜滑动特征,受控于稳定山体的阻挡;(2)顺向单斜地貌,节理裂隙发育,具有软硬夹层的地质结构是滑坡形成的控制因素;长江深切侵蚀为滑坡提供了动力基础和运动空间;集中降雨和三峡水库蓄水是导致滑坡复活的主要诱发因素;(3)各级滑坡的形成机制不同,一级滑坡为"拉裂-滑移(弯曲)-剪断"模式,二级滑坡为"平面滑移"模式,三级滑坡为"滑移-剪断"模式;(4)现阶段该滑坡以局部浅层失稳破坏形式为主。最后,建议加强三峡库区集中降雨和库水位波动联合作用下滑坡诱发机理与监测预警研究工作。     

三峡库区四方碑滑坡稳定性与变形趋势预测

三峡水库建成后,库水位周期性涨落和暴雨产生的渗流作用导致大量古滑坡的复活或新滑坡的发生。以库区近水平层状结构的四方碑滑坡为例,依据库水位实际调动,将水位从175 m至145 m不同降速与50年一遇暴雨进行工况组合,计算4种工况下滑坡的稳定性及破坏概率。然后采用Geo-studio软件的Sigma模块对滑坡进行变形模拟,运用R/S分析方法判断滑坡的变形持续性,并结合野外调查情况,综合评价分析四方碑滑坡的稳定性。结果表明:滑坡在各工况下整体均处于基本稳定状态,具有低危险性;变形模拟结果显示滑坡前缘位移最大,与野外调查情况一致;各监测点Hurst指数均介于0.5~1,表明时间序列具有正持续性,在研究的时间限度内滑坡的局部破坏增强,应在汛期加强对滑坡前缘的巡查和预警。     

The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area,China

Landslide susceptibility mapping is an indispensable prerequisite for landslide prevention and reduction. At present, research into landslide susceptibility mapping has begun to combine machine learning with remote sensing and geographic information system (GIS) techniques. The random forest model is a new integrated classification method, but its application to landslide susceptibility mapping remains limited. Landslides represent a serious threat to the lives and property of people living in the Zigui–Badong area in the Three Gorges region of China, as well as to the operation of the Three Gorges Reservoir. However, the geological structure of this region is complex, involving steep mountains and deep valleys. The purpose of the current study is to produce a landslide susceptibility map of the Zigui–Badong area using a random forest model, multisource data, GIS, and remote sensing data. In total, 300 pre-existing landslide locations were obtained from a landslide inventory map. These landslides were identified using visual interpretation of high-resolution remote sensing images, topographic and geologic data, and extensive field surveys. The occurrence of landslides is closely related to a series of environmental parameters. Topographic, geologic, Landsat-8 image, raining data, and seismic data were used as the primary data sources to extract the geo-environmental factors influencing landslides. Thirty-four layers of causative factors were prepared as predictor variables, which can mainly be categorized as topographic, geological, hydrological, land cover, and environmental trigger parameters. The random forest method is an ensemble classification technique that extends diversity among the classification trees by resampling the data with replacement and randomly changing the predictive variable sets during the different tree induction processes. A random forest model was adopted to calculate the quantitative relationships between the landslide-conditioning factors and the landslide inventory map and then generate a landslide susceptibility map. The analytical results were compared with known landslide locations in terms of area under the receiver operating characteristic curve. The random forest model has an area ratio of 86.10%. In contrast to the random forest (whole factors, WF), random forest (12 major factors, 12F), decision tree (WF), decision tree (12F), the final result shows that random forest (12F) has a higher prediction accuracy. Meanwhile, the random forest models have higher prediction accuracy than the decision tree model. Subsequently, the landslide susceptibility map was classified into five classes (very low, low, moderate, high, and very high). The results demonstrate that the random forest model achieved a reasonable accuracy in landslide susceptibility mapping. The landslide hazard zone information will be useful for general development planning and landslide risk management.